alexgenovese / test-endpoint

Use Huggingface model name to test a ANY XL model

Cold

Public
10.8K runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

base_model

string

Shift + Return to add a new line

base_model HF sintax

Default: "SG161222/RealVisXL_V1.0"

prompt

string

Shift + Return to add a new line

A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie stillA realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie still

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgustingworst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting

Input Negative Prompt

Default: ""

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7.5

seed

integer

Random seed. Leave blank to randomize the seed

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

Run this model in Node.js with one line of code:

npx create-replicate --model=alexgenovese/test-endpoint

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run alexgenovese/test-endpoint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "alexgenovese/test-endpoint:7b1b908e2e28ee517404087adadabe32602530d4f44d20cfac8941b9a05ce267",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie still",
      scheduler: "K_EULER",
      base_model: "SG161222/RealVisXL_V1.0",
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      negative_prompt: "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run alexgenovese/test-endpoint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "alexgenovese/test-endpoint:7b1b908e2e28ee517404087adadabe32602530d4f44d20cfac8941b9a05ce267",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie still",
        "scheduler": "K_EULER",
        "base_model": "SG161222/RealVisXL_V1.0",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run alexgenovese/test-endpoint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "7b1b908e2e28ee517404087adadabe32602530d4f44d20cfac8941b9a05ce267",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie still",
      "scheduler": "K_EULER",
      "base_model": "SG161222/RealVisXL_V1.0",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2023-09-22T19:44:57.083473Z",
  "created_at": "2023-09-22T19:44:42.001062Z",
  "data_removed": false,
  "error": null,
  "id": "pnrolmzbk5ldbbj5vssu2e3tky",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3. detailed face and detailed skin. cinematic lighting, movie still",
    "scheduler": "K_EULER",
    "base_model": "SG161222/RealVisXL_V1.0",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "apply_watermark": true,
    "negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
    "num_inference_steps": 50
  },
  "logs": "Using seed: 22956\nLoading sdxl pipeline...\nLoading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...:  14%|█▍        | 1/7 [00:00<00:01,  4.77it/s]\nLoading pipeline components...:  43%|████▎     | 3/7 [00:00<00:00,  6.68it/s]\nLoading pipeline components...:  71%|███████▏  | 5/7 [00:00<00:00,  9.09it/s]\nLoading pipeline components...:  86%|████████▌ | 6/7 [00:01<00:00,  4.32it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:02<00:00,  1.50it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:02<00:00,  2.36it/s]\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:07,  6.92it/s]\n  4%|▍         | 2/50 [00:00<00:06,  7.68it/s]\n  6%|▌         | 3/50 [00:00<00:06,  7.44it/s]\n  8%|▊         | 4/50 [00:00<00:06,  7.22it/s]\n 10%|█         | 5/50 [00:00<00:06,  7.13it/s]\n 12%|█▏        | 6/50 [00:00<00:06,  6.95it/s]\n 14%|█▍        | 7/50 [00:00<00:06,  7.14it/s]\n 16%|█▌        | 8/50 [00:01<00:05,  7.48it/s]\n 18%|█▊        | 9/50 [00:01<00:05,  7.58it/s]\n 20%|██        | 10/50 [00:01<00:05,  7.53it/s]\n 22%|██▏       | 11/50 [00:01<00:05,  7.44it/s]\n 24%|██▍       | 12/50 [00:01<00:05,  7.39it/s]\n 26%|██▌       | 13/50 [00:01<00:04,  7.49it/s]\n 28%|██▊       | 14/50 [00:01<00:04,  7.73it/s]\n 30%|███       | 15/50 [00:02<00:04,  7.86it/s]\n 32%|███▏      | 16/50 [00:02<00:04,  8.03it/s]\n 34%|███▍      | 17/50 [00:02<00:04,  8.18it/s]\n 36%|███▌      | 18/50 [00:02<00:03,  8.30it/s]\n 38%|███▊      | 19/50 [00:02<00:03,  8.30it/s]\n 40%|████      | 20/50 [00:02<00:03,  8.34it/s]\n 42%|████▏     | 21/50 [00:02<00:03,  8.42it/s]\n 44%|████▍     | 22/50 [00:02<00:03,  8.47it/s]\n 46%|████▌     | 23/50 [00:02<00:03,  8.26it/s]\n 48%|████▊     | 24/50 [00:03<00:03,  8.31it/s]\n 50%|█████     | 25/50 [00:03<00:02,  8.39it/s]\n 52%|█████▏    | 26/50 [00:03<00:02,  8.45it/s]\n 54%|█████▍    | 27/50 [00:03<00:02,  8.48it/s]\n 56%|█████▌    | 28/50 [00:03<00:02,  8.51it/s]\n 58%|█████▊    | 29/50 [00:03<00:02,  8.53it/s]\n 60%|██████    | 30/50 [00:03<00:02,  8.55it/s]\n 62%|██████▏   | 31/50 [00:03<00:02,  8.56it/s]\n 64%|██████▍   | 32/50 [00:04<00:02,  8.52it/s]\n 66%|██████▌   | 33/50 [00:04<00:01,  8.54it/s]\n 68%|██████▊   | 34/50 [00:04<00:01,  8.56it/s]\n 70%|███████   | 35/50 [00:04<00:01,  8.57it/s]\n 72%|███████▏  | 36/50 [00:04<00:01,  8.58it/s]\n 74%|███████▍  | 37/50 [00:04<00:01,  8.54it/s]\n 76%|███████▌  | 38/50 [00:04<00:01,  8.38it/s]\n 78%|███████▊  | 39/50 [00:04<00:01,  8.27it/s]\n 80%|████████  | 40/50 [00:04<00:01,  8.14it/s]\n 82%|████████▏ | 41/50 [00:05<00:01,  8.27it/s]\n 84%|████████▍ | 42/50 [00:05<00:00,  8.37it/s]\n 86%|████████▌ | 43/50 [00:05<00:00,  8.37it/s]\n 88%|████████▊ | 44/50 [00:05<00:00,  8.43it/s]\n 90%|█████████ | 45/50 [00:05<00:00,  8.48it/s]\n 92%|█████████▏| 46/50 [00:05<00:00,  8.52it/s]\n 94%|█████████▍| 47/50 [00:05<00:00,  8.54it/s]\n 96%|█████████▌| 48/50 [00:05<00:00,  8.56it/s]\n 98%|█████████▊| 49/50 [00:06<00:00,  8.13it/s]\n100%|██████████| 50/50 [00:06<00:00,  8.26it/s]\n100%|██████████| 50/50 [00:06<00:00,  8.12it/s]",
  "metrics": {
    "predict_time": 15.113188,
    "total_time": 15.082411
  },
  "output": [
    "https://replicate.delivery/pbxt/nuOfFs3YzoWbAavGUQ4kB1m50hvGuXplffehCLuKY5djLobGB/out-0.png"
  ],
  "started_at": "2023-09-22T19:44:41.970285Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/pnrolmzbk5ldbbj5vssu2e3tky",
    "cancel": "https://api.replicate.com/v1/predictions/pnrolmzbk5ldbbj5vssu2e3tky/cancel"
  },
  "version": "cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764"
}

Generated in

15.1 seconds

Tweak itReport

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This output was created using a different version of the model, alexgenovese/test-endpoint:cacd901f.

Examples

View more examples

Run time and cost

This model costs approximately $0.016 to run on Replicate, or 62 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 12 seconds.

Readme

Test ANY diffuser model on Huggingface.com

How it works

Copy the model you want to test. Example: SG161222/RealVisXL_V4.0
Fill the form
Run the inference